Cognitive control exertion leads to reductions in peak power output and as well as increased perceived exertion on a graded exercise test to exhaustion
Bibliographic record
Abstract
We investigated effects of a brief (10.5 min) cognitively demanding task on graded exercise test performance. Untrained, university students (N = 15) completed two graded exercise tests in counterbalanced, randomised order. One test was preceded by restful viewing of a documentary video (control); the other by a stop-signal task. Cardiorespiratory functions and perceived exertion were monitored during exercise. Peak power output (W) was lower following the stop-signal task (M = 240.03, SD = 53.37) compared to control (M = 246.03, SD = 52.60), P = 0.002, ηP2 = 0.493, as was (P = 0.042, Cohen’s d = 0.55). Perceived exertion was significantly higher at 50% (d = 0.77) and 75% (d = 0.83) of iso-time following the cognitive task (Ps ≤ 0.01). Results are consistent with research showing negative carryover effects of cognitively demanding tasks on whole-body endurance performance. Results also support the psychobiological model of exercise as performance of the cognitive task did not affect perceived exertion when exercise task demands were lower, but lead to greater perceived exertion and earlier withdrawal of effort at higher levels of exercise task demand. Findings have implications for understanding psychological determinants of exercise performance and conditions that may lead to underestimation of .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".